The present invention relates to suppressing dynamic interference in radar and digital communications systems, and, in particular, relates to two-dimensional adaptive processing techniques applied to such systems. Interference is defined as clutter, jamming, or any other form of unwanted electromagnetic energy that can mask a desired target. Clutter is further defined as unwanted backscatter from the earth's surface and atmosphere or moving objects in either realm.
The growth of wireless communications is rapidly turning the communications spectrum into an environment of dynamic interference, much of it severe. The typical wireless receiver in a large city must contend with hundreds, perhaps thousands, of transmissions. All transmissions, other than the one directed to the particular receiver, are perceived as interference.
This interference is currently countered through the use of available spectral bandwidth and Code Division Multiple Access ("CDMA"). However, bandwidth is limited, and, as wireless communications increase, interference will increase substantially, to the point that the current techniques to avoid or eliminate interference will not work.
Space-Time Adaptive Processing ("STAP") techniques suppress the dynamic interference encountered by airborne surveillance radars. These STAP techniques have reduced interference in wireless communications (T. S. Rappaport, Wireless Communications: Principles and Practice. Englewood Cliffs, N.J., 1996; J. C. Liberti and T. S. Rappaport, Smart Antennas for Wireless Communications: IS-95 and Third Generation CDMA Applications. Upper Saddle River, N.J., 1997).
STAP techniques adaptively combine data from several pulses and antenna elements to suppress interference. Each STAP technique uses a distinct algorithm to calculate the adaptive weights applied to the returns from each element and pulse. These weights are calculated to give maximum response from an antenna, a "main beam", at a chosen look angle and normalized look-Doppler frequency while simultaneously suppressing interference. The weights are applied through multipliers to obtain weighted returns. The weighted returns are then added together to form a single output. If the output exceeds a threshold value, a target is deemed to be present.
In the prior art, adaptive weights are calculated by two broad classes of algorithms: statistical and direct data domain. Each has advantages and drawbacks.
Statistical STAP techniques succeed because the Coherent Processing Interval ("CPI"), or adaptive dwell time, is short enough that the interference environment does not change. The adaptive dwell time determines how long data is collected before a new set of adaptive weights is calculated. Within this adaptive dwell time, the STAP technique estimates the interference and calculates the weights that suppress it. If the interference changes within the adaptive period, the interference estimate will be corrupt and the resulting filter mismatched to the interference.
Statistical algorithms fail when the secondary data does not reflect the statistics of the interference in the range cell of interest, i.e., when the data is non-homogeneous. This situation occurs when the CPI length is too long, allowing the interference to change within the adaptive dwell time. However, both an airborne radar and a communications system commonly encounter non-homogeneous data no matter what the CPI length. In many real-world situations, e.g., airborne surveillance over land-sea interfaces, dense target environments, the data is non-homogeneous.
Purely statistical STAP techniques for airborne radar estimate the interference within the range cell of interest from the surrounding range cells. In a communications system, the covariance matrix of the interference is estimated from the entire data block. An adaptive filter that suppresses the interference is generated from this estimate by second-order statistics. This technique works only if the interference statistics in the surrounding range cells accurately reflect the interference statistics in the range cell of interest. That is, the data must be independent, identically distributed ("i.i.d."), or homogeneous, data.
The reverse of homogeneity, non-homogeneity, occurs commonly in real-world radar transmissions. Non-homogeneous data is defined as that from any range cell or cells whose interference statistics are not identical to the other range cells within the data set. The obvious example is a discrete interferer or target. Other examples include terrain transitions, such as going from sea to land or from flat desert to mountains. Any interference that is not i.i.d. is non-homogeneous.
One example of non-homogeneous data is a strong return signal through a sidelobe that does not correspond in either angle or Doppler to the look direction of the radar. In this example, the return is known as a discrete interferer. When the radar is looking in a direction, indicated by the mainbeam, other than that of the discrete interferer, the interferer can mask a small target or give a false indication of a target where one does not exist. In a communications system, this situation is characterized, not by "false alarms", but by bleed-over from another conversation or data transmission. High sidelobes commonly cause problems of discrete interference for STAP techniques.
Methods currently exist to detect non-homogeneities within a data set (M. C. Wicks, W. L. Melvin, and P. Chen, "An efficient architecture for nonhomogeneity detection in space-time adaptive processing for airborne early warning radar," Proceedings of the 1997 IEE Radar Conference, October 1997, Edinburgh, UK; W. L. Melvin and M. C. Wicks, "Improving practical space-time adaptive radar," Proceedings of the 1997 IEEE National Radar conference, May 1997. Syracuse, N.Y.; R. S. Adve, T. B. Hale, and M. C. Wicks, "Transform domain localized processing using measured steering vectors and non-homogeneity detection," Proceedings of the 1999 IEEE National Radar Conference, April 1999, Boston, Mass.). However, none of the methods for detecting non-homogeneities address what to do with cells that contain non-homogeneous data. Non-homogeneity means that the statistics within a particular data cell are not reflected in surrounding data cells. Thus statistical algorithms fail with such data.
The inability of statistical STAP algorithms to deal with non-homogeneities in the range cell of interest led us to consider non-statistical or direct data domain algorithms. These algorithms take data from only the range cell of interest, thereby suppressing discrete interferers within that range cell and eliminating the sample support problems associated with statistical approaches.
Research on direct data domain algorithms has focused on one-dimensional spatial adaptivity (T. K. Sarkar and N. Sangruji, "An adaptive nulling system for a narrow-band signal with a look-direction constraint utilizing the conjugate gradient method," IEEE Transactions on Antennas and Propagation 37: 940-944 (July 1989); S. Park and T. K. Sarkar, "A deterministic eigenvalue approach to space time adaptive processing," Proceedings of the IEEE Antennas and Propagation Society International Symposium, 1168-1171 (July 1996)). All direct data domain algorithms currently in STAP techniques are one-dimensional. Thus they are incapable of remedying the defects of statistical STAP techniques.
Thus there is need for a STAP technique that overcomes the drawbacks of the prior art by combining statistical and non-statistical (i.e., direct data domain) algorithms into a hybrid.